Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Human-Computer Interaction

arXiv:2206.15000 (cs)
[Submitted on 30 Jun 2022 (v1), last revised 31 Oct 2022 (this version, v3)]

Title:Grounded Copilot: How Programmers Interact with Code-Generating Models

Authors:Shraddha Barke, Michael B. James, Nadia Polikarpova
View PDF
Abstract:Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants--with a range of prior experience using the assistant--as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.
Subjects: Human-Computer Interaction (cs.HC); Programming Languages (cs.PL)
Cite as: arXiv:2206.15000 [cs.HC]
  (or arXiv:2206.15000v3 [cs.HC] for this version)
  https://j8ftcxlxtb.proxynodejs.usequeue.com/10.48550/arXiv.2206.15000

Submission history

From: Michael B. James [view email]
[v1] Thu, 30 Jun 2022 03:18:03 UTC (998 KB)
[v2] Tue, 2 Aug 2022 23:30:40 UTC (5,999 KB)
[v3] Mon, 31 Oct 2022 16:38:09 UTC (2,982 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.HC
< prev   |   next >
new | recent | 2022-06
Change to browse by:
cs
cs.PL

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)